Overview

Dataset statistics

Number of variables26
Number of observations2772
Missing cells2049
Missing cells (%)2.8%
Duplicate rows2
Duplicate rows (%)0.1%
Total size in memory2.3 MiB
Average record size in memory865.5 B

Variable types

NUM15
CAT10
URL1

Warnings

county has constant value "2772" Constant
Dataset has 2 (0.1%) duplicate rows Duplicates
address has a high cardinality: 2754 distinct values High cardinality
subdivision has a high cardinality: 2235 distinct values High cardinality
lot has a high cardinality: 346 distinct values High cardinality
zoning has a high cardinality: 696 distinct values High cardinality
date has a high cardinality: 271 distinct values High cardinality
bathrooms is highly correlated with home_size and 1 other fieldsHigh correlation
home_size is highly correlated with bathroomsHigh correlation
bedrooms is highly correlated with bathroomsHigh correlation
latitude has 35 (1.3%) missing values Missing
longitude has 35 (1.3%) missing values Missing
home_size has 129 (4.7%) missing values Missing
lot_size has 45 (1.6%) missing values Missing
year_built has 107 (3.9%) missing values Missing
subdivision has 202 (7.3%) missing values Missing
census has 32 (1.2%) missing values Missing
tract has 32 (1.2%) missing values Missing
lot has 178 (6.4%) missing values Missing
sale_price has 97 (3.5%) missing values Missing
estimated_value has 246 (8.9%) missing values Missing
crime_index has 416 (15.0%) missing values Missing
school quality has 41 (1.5%) missing values Missing
bedrooms has 218 (7.9%) missing values Missing
bathrooms has 218 (7.9%) missing values Missing
lot_size is highly skewed (γ1 = 37.90033381) Skewed
address is uniformly distributed Uniform
subdivision is uniformly distributed Uniform
tract has 29 (1.0%) zeros Zeros
sex_offenders has 472 (17.0%) zeros Zeros

Reproduction

Analysis started2020-10-11 04:48:11.245389
Analysis finished2020-10-11 04:49:17.710110
Duration1 minute and 6.46 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

latitude
Real number (ℝ≥0)

MISSING

Distinct2641
Distinct (%)96.5%
Missing35
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean34.11506348
Minimum33.339579
Maximum34.818751
Zeros0
Zeros (%)0.0%
Memory size21.8 KiB
2020-10-10T21:49:17.894192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum33.339579
5-th percentile33.7895664
Q133.97726
median34.091858
Q334.19718
95-th percentile34.6227896
Maximum34.818751
Range1.479172
Interquartile range (IQR)0.21992

Descriptive statistics

Standard deviation0.2229458875
Coefficient of variation (CV)0.006535115717
Kurtosis0.6599383568
Mean34.11506348
Median Absolute Deviation (MAD)0.109896
Skewness0.7845505351
Sum93372.92873
Variance0.04970486875
MonotocityNot monotonic
2020-10-10T21:49:18.113930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
34.6881170.6%
 
34.6867110.4%
 
34.147933100.4%
 
34.529380.3%
 
34.597660.2%
 
34.275440.1%
 
34.486340.1%
 
33.7890230.1%
 
34.16791520.1%
 
34.15407920.1%
 
34.16981920.1%
 
33.97475620.1%
 
34.18440320.1%
 
33.78915320.1%
 
34.496620.1%
 
34.3716720.1%
 
34.41361320.1%
 
33.77160820.1%
 
34.04662420.1%
 
34.1408720.1%
 
33.95240720.1%
 
34.12851820.1%
 
33.9581820.1%
 
34.15081820.1%
 
34.103620.1%
 
Other values (2616)264095.2%
 
(Missing)351.3%
 
ValueCountFrequency (%) 
33.3395791< 0.1%
 
33.3399011< 0.1%
 
33.3543091< 0.1%
 
33.7072441< 0.1%
 
33.712041< 0.1%
 
33.7194111< 0.1%
 
33.7203351< 0.1%
 
33.7222181< 0.1%
 
33.7231151< 0.1%
 
33.723881< 0.1%
 
ValueCountFrequency (%) 
34.8187511< 0.1%
 
34.7940871< 0.1%
 
34.7787251< 0.1%
 
34.73957120.1%
 
34.7178491< 0.1%
 
34.7167991< 0.1%
 
34.7139531< 0.1%
 
34.7134281< 0.1%
 
34.7131461< 0.1%
 
34.7127921< 0.1%
 

longitude
Real number (ℝ)

MISSING

Distinct2640
Distinct (%)96.5%
Missing35
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean-118.2729196
Minimum-118.860729
Maximum-117.695795
Zeros0
Zeros (%)0.0%
Memory size21.8 KiB
2020-10-10T21:49:18.362390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-118.860729
5-th percentile-118.5835502
Q1-118.432931
median-118.299702
Q3-118.129625
95-th percentile-117.8656416
Maximum-117.695795
Range1.164934
Interquartile range (IQR)0.303306

Descriptive statistics

Standard deviation0.2156970722
Coefficient of variation (CV)-0.001823723241
Kurtosis-0.260077809
Mean-118.2729196
Median Absolute Deviation (MAD)0.149962
Skewness0.372158866
Sum-323712.9809
Variance0.04652522697
MonotocityNot monotonic
2020-10-10T21:49:18.606171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-118.0618170.6%
 
-118.2375110.4%
 
-118.414091100.4%
 
-117.963280.3%
 
-117.839860.2%
 
-118.550540.1%
 
-118.197440.1%
 
-118.05943730.1%
 
-118.13512130.1%
 
-118.41444420.1%
 
-118.35890120.1%
 
-118.13357520.1%
 
-118.13493120.1%
 
-118.21761820.1%
 
-118.42410320.1%
 
-118.45668620.1%
 
-118.24980120.1%
 
-118.16146420.1%
 
-118.18038520.1%
 
-117.72279820.1%
 
-118.34046920.1%
 
-118.32842120.1%
 
-117.82199120.1%
 
-118.21980820.1%
 
-118.29477120.1%
 
Other values (2615)263995.2%
 
(Missing)351.3%
 
ValueCountFrequency (%) 
-118.8607291< 0.1%
 
-118.8529171< 0.1%
 
-118.8525381< 0.1%
 
-118.8335311< 0.1%
 
-118.8328011< 0.1%
 
-118.8256621< 0.1%
 
-118.8162311< 0.1%
 
-118.8137931< 0.1%
 
-118.7943261< 0.1%
 
-118.7939561< 0.1%
 
ValueCountFrequency (%) 
-117.6957951< 0.1%
 
-117.6995321< 0.1%
 
-117.7037991< 0.1%
 
-117.7087051< 0.1%
 
-117.7125371< 0.1%
 
-117.7153041< 0.1%
 
-117.7182611< 0.1%
 
-117.7196781< 0.1%
 
-117.7205761< 0.1%
 
-117.7218681< 0.1%
 

address
Categorical

HIGH CARDINALITY
UNIFORM

Distinct2754
Distinct (%)99.5%
Missing5
Missing (%)0.2%
Memory size21.8 KiB
Vac/pillsbury St/vic Trevor Ave
 
4
Vac/donatello St/vic Lamour Ct
 
3
Vac/soledad Canyon Rd/vic Crow Vly
 
3
2107 Abrazo Dr
 
2
Vac/ave R6/vic Longview Rd
 
2
Other values (2749)
2753 
ValueCountFrequency (%) 
Vac/pillsbury St/vic Trevor Ave40.1%
 
Vac/donatello St/vic Lamour Ct30.1%
 
Vac/soledad Canyon Rd/vic Crow Vly30.1%
 
2107 Abrazo Dr20.1%
 
Vac/ave R6/vic Longview Rd20.1%
 
Vac/170th/171st Ste/vic Park Vly20.1%
 
6300 Chalet Dr20.1%
 
24421 Shadeland Dr20.1%
 
330 W Pillsbury St20.1%
 
1417 Cole Pl1< 0.1%
 
664 S Chaparro Rd1< 0.1%
 
4901 E Ferro St1< 0.1%
 
777 S Citrus Ave # 2351< 0.1%
 
80 S Sunnyslope Ave1< 0.1%
 
904 Knob Hill Ave1< 0.1%
 
1409 Junipero Ave1< 0.1%
 
2819 Frederick St1< 0.1%
 
917 S Sierra Bonita Ave1< 0.1%
 
28456 Mirabelle Ln1< 0.1%
 
20350 Damietta Dr1< 0.1%
 
22314 Barcotta Dr1< 0.1%
 
820 S Magnolia Ave1< 0.1%
 
5403 Autry Ave1< 0.1%
 
1884 Caminito De La Narcissa1< 0.1%
 
7700 Satsuma Ave1< 0.1%
 
Other values (2729)272998.4%
 
(Missing)50.2%
 
2020-10-10T21:49:18.871880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2745 ?
Unique (%)99.2%
2020-10-10T21:49:19.107133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length34
Median length17
Mean length17.63455988
Min length3

Overview of Unicode Properties

Unique unicode characters66
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
765315.7%
 
e30826.3%
 
123024.7%
 
a21824.5%
 
218363.8%
 
r18033.7%
 
t17613.6%
 
n17213.5%
 
l15313.1%
 
014643.0%
 
o14523.0%
 
v14092.9%
 
313952.9%
 
413042.7%
 
i12302.5%
 
A11842.4%
 
511582.4%
 
S11222.3%
 
d9351.9%
 
69081.9%
 
78321.7%
 
87751.6%
 
s7081.4%
 
96801.4%
 
h5361.1%
 
Other values (41)792016.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter2139243.8%
 
Decimal Number1265425.9%
 
Space Separator765315.7%
 
Uppercase Letter660313.5%
 
Other Punctuation5791.2%
 
Dash Punctuation2< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1230218.2%
 
2183614.5%
 
0146411.6%
 
3139511.0%
 
4130410.3%
 
511589.2%
 
69087.2%
 
78326.6%
 
87756.1%
 
96805.4%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
7653100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A118417.9%
 
S112217.0%
 
C4927.5%
 
D4767.2%
 
W3936.0%
 
B3635.5%
 
L3104.7%
 
R2894.4%
 
E2664.0%
 
P2563.9%
 
V2213.3%
 
M2193.3%
 
N1862.8%
 
H1672.5%
 
G1352.0%
 
O1111.7%
 
F1081.6%
 
T1051.6%
 
K671.0%
 
J560.8%
 
I400.6%
 
Q120.2%
 
Y100.2%
 
Z70.1%
 
U50.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e308214.4%
 
a218210.2%
 
r18038.4%
 
t17618.2%
 
n17218.0%
 
l15317.2%
 
o14526.8%
 
v14096.6%
 
i12305.7%
 
d9354.4%
 
s7083.3%
 
h5362.5%
 
c4622.2%
 
u4382.0%
 
y4292.0%
 
m3541.7%
 
g2721.3%
 
w2521.2%
 
k2201.0%
 
b2020.9%
 
p1700.8%
 
f1530.7%
 
z440.2%
 
x270.1%
 
j120.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
#47381.7%
 
/10618.3%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-2100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin2799557.3%
 
Common2088842.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
765336.6%
 
1230211.0%
 
218368.8%
 
014647.0%
 
313956.7%
 
413046.2%
 
511585.5%
 
69084.3%
 
78324.0%
 
87753.7%
 
96803.3%
 
#4732.3%
 
/1060.5%
 
-2< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e308211.0%
 
a21827.8%
 
r18036.4%
 
t17616.3%
 
n17216.1%
 
l15315.5%
 
o14525.2%
 
v14095.0%
 
i12304.4%
 
A11844.2%
 
S11224.0%
 
d9353.3%
 
s7082.5%
 
h5361.9%
 
C4921.8%
 
D4761.7%
 
c4621.7%
 
u4381.6%
 
y4291.5%
 
W3931.4%
 
B3631.3%
 
m3541.3%
 
L3101.1%
 
R2891.0%
 
g2721.0%
 
Other values (27)306110.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII48883100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
765315.7%
 
e30826.3%
 
123024.7%
 
a21824.5%
 
218363.8%
 
r18033.7%
 
t17613.6%
 
n17213.5%
 
l15313.1%
 
014643.0%
 
o14523.0%
 
v14092.9%
 
313952.9%
 
413042.7%
 
i12302.5%
 
A11842.4%
 
511582.4%
 
S11222.3%
 
d9351.9%
 
69081.9%
 
78321.7%
 
87751.6%
 
s7081.4%
 
96801.4%
 
h5361.1%
 
Other values (41)792016.2%
 

property_type
Categorical

Distinct34
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size21.8 KiB
Single Family Residence
1716 
Condominium
573 
Duplex (2 units, any combination)
 
83
Planned Unit Development (PUD)
 
81
Residential - Vacant Land
 
59
Other values (29)
260 
ValueCountFrequency (%) 
Single Family Residence171661.9%
 
Condominium57320.7%
 
Duplex (2 units, any combination)833.0%
 
Planned Unit Development (PUD)812.9%
 
Residential - Vacant Land592.1%
 
Apartment house (5+ units)381.4%
 
Triplex (3 units, any combination)321.2%
 
MISCELLANEOUS (Vacant Land) 240.9%
 
Quadplex (4 Units, Any Combination)240.9%
 
Light Industrial (10% Improved Office space; Machine Shop)200.7%
 
Warehouse, Storage200.7%
 
Store, Retail Outlet 180.6%
 
MISCELLANEOUS (Commercial)140.5%
 
Office Building120.4%
 
Vacant Commercial100.4%
 
Gas Station70.3%
 
Industrial - Vacant Land70.3%
 
Parking Garage, Parking Structure60.2%
 
Restaurant40.1%
 
Store/Office (mixed use)30.1%
 
Community: Shopping Center, Mini-Mall30.1%
 
Parcel Number30.1%
 
Public School (administration, campus, dorms, instruction)30.1%
 
Mobile home20.1%
 
Religious, Church, Worship (Synagogue, Temple, Parsonage)1< 0.1%
 
Other values (9)90.3%
 
2020-10-10T21:49:19.345935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique10 ?
Unique (%)0.4%
2020-10-10T21:49:19.564143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length58
Median length23
Mean length21.62626263
Min length10

Overview of Unicode Properties

Unique unicode characters59
Unique unicode categories9 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e782113.0%
 
i721912.0%
 
n59039.8%
 
51168.5%
 
l39246.5%
 
m32155.4%
 
a26904.5%
 
d26144.4%
 
s20943.5%
 
c20603.4%
 
S18743.1%
 
y18603.1%
 
R18003.0%
 
g17973.0%
 
F17182.9%
 
o17172.9%
 
u9911.7%
 
t9201.5%
 
C6681.1%
 
p3310.6%
 
(3260.5%
 
)3260.5%
 
r2650.4%
 
D2450.4%
 
U2250.4%
 
Other values (34)22293.7%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter4603176.8%
 
Uppercase Letter757212.6%
 
Space Separator51168.5%
 
Open Punctuation3260.5%
 
Close Punctuation3260.5%
 
Other Punctuation2520.4%
 
Decimal Number2170.4%
 
Dash Punctuation700.1%
 
Math Symbol380.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S187424.7%
 
R180023.8%
 
F171822.7%
 
C6688.8%
 
D2453.2%
 
U2253.0%
 
L1862.5%
 
P1842.4%
 
V1031.4%
 
A1001.3%
 
O911.2%
 
I861.1%
 
E761.0%
 
M670.9%
 
N410.5%
 
T330.4%
 
Q240.3%
 
W210.3%
 
G140.2%
 
B130.2%
 
H3< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e782117.0%
 
i721915.7%
 
n590312.8%
 
l39248.5%
 
m32157.0%
 
a26905.8%
 
d26145.7%
 
s20944.5%
 
c20604.5%
 
y18604.0%
 
g17973.9%
 
o17173.7%
 
u9912.2%
 
t9202.0%
 
p3310.7%
 
r2650.6%
 
b1490.3%
 
x1420.3%
 
h1310.3%
 
v1050.2%
 
f700.2%
 
k13< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
5116100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(326100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
28338.2%
 
53817.5%
 
33214.7%
 
42411.1%
 
1209.2%
 
0209.2%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
+38100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)326100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,20079.4%
 
%207.9%
 
;207.9%
 
/93.6%
 
:31.2%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-70100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin5360389.4%
 
Common634510.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e782114.6%
 
i721913.5%
 
n590311.0%
 
l39247.3%
 
m32156.0%
 
a26905.0%
 
d26144.9%
 
s20943.9%
 
c20603.8%
 
S18743.5%
 
y18603.5%
 
R18003.4%
 
g17973.4%
 
F17183.2%
 
o17173.2%
 
u9911.8%
 
t9201.7%
 
C6681.2%
 
p3310.6%
 
r2650.5%
 
D2450.5%
 
U2250.4%
 
L1860.3%
 
P1840.3%
 
b1490.3%
 
Other values (18)11332.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
511680.6%
 
(3265.1%
 
)3265.1%
 
,2003.2%
 
2831.3%
 
-701.1%
 
5380.6%
 
+380.6%
 
3320.5%
 
4240.4%
 
1200.3%
 
0200.3%
 
%200.3%
 
;200.3%
 
/90.1%
 
:3< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII59948100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e782113.0%
 
i721912.0%
 
n59039.8%
 
51168.5%
 
l39246.5%
 
m32155.4%
 
a26904.5%
 
d26144.4%
 
s20943.5%
 
c20603.4%
 
S18743.1%
 
y18603.1%
 
R18003.0%
 
g17973.0%
 
F17182.9%
 
o17172.9%
 
u9911.7%
 
t9201.5%
 
C6681.1%
 
p3310.6%
 
(3260.5%
 
)3260.5%
 
r2650.4%
 
D2450.4%
 
U2250.4%
 
Other values (34)22293.7%
 

home_size
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1633
Distinct (%)61.8%
Missing129
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean2390.203027
Minimum240
Maximum136778.4
Zeros0
Zeros (%)0.0%
Memory size21.8 KiB
2020-10-10T21:49:19.763361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum240
5-th percentile837.2
Q11243
median1624
Q32278.5
95-th percentile4672.9
Maximum136778.4
Range136538.4
Interquartile range (IQR)1035.5

Descriptive statistics

Standard deviation5548.576753
Coefficient of variation (CV)2.321383033
Kurtosis307.4538344
Mean2390.203027
Median Absolute Deviation (MAD)456
Skewness16.02730976
Sum6317306.6
Variance30786703.98
MonotocityNot monotonic
2020-10-10T21:49:19.966876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
144090.3%
 
177080.3%
 
96080.3%
 
183080.3%
 
154080.3%
 
160070.3%
 
157070.3%
 
158070.3%
 
120060.2%
 
157660.2%
 
94060.2%
 
147860.2%
 
125060.2%
 
173460.2%
 
126060.2%
 
164660.2%
 
126460.2%
 
121060.2%
 
140050.2%
 
209050.2%
 
120450.2%
 
150050.2%
 
117650.2%
 
108050.2%
 
158550.2%
 
Other values (1608)248689.7%
 
(Missing)1294.7%
 
ValueCountFrequency (%) 
2401< 0.1%
 
3601< 0.1%
 
4601< 0.1%
 
4691< 0.1%
 
4761< 0.1%
 
48020.1%
 
5001< 0.1%
 
5031< 0.1%
 
5151< 0.1%
 
5161< 0.1%
 
ValueCountFrequency (%) 
136778.41< 0.1%
 
111949.21< 0.1%
 
110206.81< 0.1%
 
102801.61< 0.1%
 
71438.41< 0.1%
 
69260.41< 0.1%
 
60112.81< 0.1%
 
57063.61< 0.1%
 
33105.61< 0.1%
 
32234.41< 0.1%
 

lot_size
Real number (ℝ≥0)

MISSING
SKEWED

Distinct2158
Distinct (%)79.1%
Missing45
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean41881.3125
Minimum745
Maximum12486474
Zeros0
Zeros (%)0.0%
Memory size21.8 KiB
2020-10-10T21:49:20.188089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum745
5-th percentile3749.3
Q16031.5
median7654
Q316368
95-th percentile172933.2
Maximum12486474
Range12485729
Interquartile range (IQR)10336.5

Descriptive statistics

Standard deviation267758.3191
Coefficient of variation (CV)6.393264754
Kurtosis1721.328249
Mean41881.3125
Median Absolute Deviation (MAD)2419
Skewness37.90033381
Sum114210339.2
Variance7.169451744e+10
MonotocityNot monotonic
2020-10-10T21:49:20.905012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5000110.4%
 
13894100.4%
 
2613690.3%
 
40946.490.3%
 
24829.280.3%
 
30056.480.3%
 
600070.3%
 
22215.670.3%
 
23086.860.2%
 
700060.2%
 
2395860.2%
 
10890060.2%
 
749950.2%
 
43995.650.2%
 
2831450.2%
 
3049250.2%
 
46173.650.2%
 
37461.650.2%
 
109335.650.2%
 
36154.850.2%
 
34412.450.2%
 
600250.2%
 
675150.2%
 
23522.450.2%
 
44431.250.2%
 
Other values (2133)256992.7%
 
(Missing)451.6%
 
ValueCountFrequency (%) 
7451< 0.1%
 
9561< 0.1%
 
9571< 0.1%
 
9861< 0.1%
 
10331< 0.1%
 
10731< 0.1%
 
10901< 0.1%
 
12001< 0.1%
 
12331< 0.1%
 
12661< 0.1%
 
ValueCountFrequency (%) 
124864741< 0.1%
 
3168554.41< 0.1%
 
1775505.61< 0.1%
 
1660507.21< 0.1%
 
1495414.81< 0.1%
 
1000573.230.1%
 
978793.21< 0.1%
 
8646661< 0.1%
 
843757.220.1%
 
842450.420.1%
 

year_built
Real number (ℝ≥0)

MISSING

Distinct123
Distinct (%)4.6%
Missing107
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean1964.194371
Minimum1890
Maximum2020
Zeros0
Zeros (%)0.0%
Memory size21.8 KiB
2020-10-10T21:49:21.128599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1890
5-th percentile1922
Q11948
median1961
Q31984
95-th percentile2008
Maximum2020
Range130
Interquartile range (IQR)36

Descriptive statistics

Standard deviation26.58381919
Coefficient of variation (CV)0.01353421004
Kurtosis-0.6178463515
Mean1964.194371
Median Absolute Deviation (MAD)19
Skewness0.04409318344
Sum5234578
Variance706.6994428
MonotocityNot monotonic
2020-10-10T21:49:21.360696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1950652.3%
 
1955642.3%
 
1952632.3%
 
1954622.2%
 
1948602.2%
 
1953552.0%
 
1956541.9%
 
1989541.9%
 
1923491.8%
 
1949471.7%
 
1973461.7%
 
1963461.7%
 
1951451.6%
 
1979451.6%
 
1957431.6%
 
1959421.5%
 
1924401.4%
 
1990401.4%
 
1947391.4%
 
1981391.4%
 
1962381.4%
 
1987371.3%
 
1988371.3%
 
1964361.3%
 
1958361.3%
 
Other values (98)148353.5%
 
(Missing)1073.9%
 
ValueCountFrequency (%) 
18901< 0.1%
 
18921< 0.1%
 
189520.1%
 
18981< 0.1%
 
18991< 0.1%
 
190120.1%
 
190420.1%
 
190540.1%
 
190660.2%
 
190720.1%
 
ValueCountFrequency (%) 
202060.2%
 
2019200.7%
 
2018110.4%
 
2017100.4%
 
2016130.5%
 
2015140.5%
 
201490.3%
 
201370.3%
 
201270.3%
 
201160.2%
 

parcel_number
Real number (ℝ≥0)

Distinct2770
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4999796552
Minimum2004009012
Maximum8765016012
Zeros0
Zeros (%)0.0%
Memory size21.8 KiB
2020-10-10T21:49:21.598284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2004009012
5-th percentile2167003562
Q12861048593
median5065015519
Q37137011783
95-th percentile8504471170
Maximum8765016012
Range6761007000
Interquartile range (IQR)4275963190

Descriptive statistics

Standard deviation2127220471
Coefficient of variation (CV)0.4254614061
Kurtosis-1.252870116
Mean4999796552
Median Absolute Deviation (MAD)2094500488
Skewness0.241551561
Sum1.385943604e+13
Variance4.525066934e+18
MonotocityNot monotonic
2020-10-10T21:49:21.818171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
282702101220.1%
 
849304002220.1%
 
40480010231< 0.1%
 
25600240021< 0.1%
 
75740030471< 0.1%
 
53020030461< 0.1%
 
26610160311< 0.1%
 
62780040671< 0.1%
 
53470180821< 0.1%
 
83900190581< 0.1%
 
42480250371< 0.1%
 
51390211491< 0.1%
 
73590231481< 0.1%
 
42870080891< 0.1%
 
22870110301< 0.1%
 
30011300401< 0.1%
 
62130210111< 0.1%
 
30240220061< 0.1%
 
82500110221< 0.1%
 
80080090381< 0.1%
 
22840040271< 0.1%
 
30800070171< 0.1%
 
87340230211< 0.1%
 
71010010311< 0.1%
 
26840160041< 0.1%
 
Other values (2745)274599.0%
 
ValueCountFrequency (%) 
20040090121< 0.1%
 
20040150181< 0.1%
 
20050170221< 0.1%
 
20070080521< 0.1%
 
20120270151< 0.1%
 
20130260281< 0.1%
 
20140050221< 0.1%
 
20140090051< 0.1%
 
20140330501< 0.1%
 
20170280401< 0.1%
 
ValueCountFrequency (%) 
87650160121< 0.1%
 
87650130241< 0.1%
 
87640050261< 0.1%
 
87640011091< 0.1%
 
87620310441< 0.1%
 
87450130321< 0.1%
 
87420110151< 0.1%
 
87400060441< 0.1%
 
87350320141< 0.1%
 
87340230211< 0.1%
 

realtyID
Real number (ℝ≥0)

Distinct2769
Distinct (%)99.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1110925272
Minimum1110722482
Maximum1111084198
Zeros0
Zeros (%)0.0%
Memory size21.8 KiB
2020-10-10T21:49:22.074641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1110722482
5-th percentile1110769088
Q11110849272
median1110893195
Q31111016228
95-th percentile1111076170
Maximum1111084198
Range361716
Interquartile range (IQR)166955.5

Descriptive statistics

Standard deviation101283.5064
Coefficient of variation (CV)9.117040453e-05
Kurtosis-1.181073077
Mean1110925272
Median Absolute Deviation (MAD)83121
Skewness-0.053158648
Sum3.078373928e+12
Variance1.025834867e+10
MonotocityNot monotonic
2020-10-10T21:49:22.307053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
111100506120.1%
 
111100534720.1%
 
11107319301< 0.1%
 
11110580621< 0.1%
 
11110752881< 0.1%
 
11108353091< 0.1%
 
11107711651< 0.1%
 
11108538151< 0.1%
 
11108762331< 0.1%
 
11108373281< 0.1%
 
11109699431< 0.1%
 
11108791571< 0.1%
 
11107382801< 0.1%
 
11108400731< 0.1%
 
11107726401< 0.1%
 
11110669391< 0.1%
 
11107930171< 0.1%
 
11108608691< 0.1%
 
11108414231< 0.1%
 
11107900991< 0.1%
 
11107666131< 0.1%
 
11107795301< 0.1%
 
11108820571< 0.1%
 
11108924481< 0.1%
 
11108475681< 0.1%
 
Other values (2744)274499.0%
 
ValueCountFrequency (%) 
11107224821< 0.1%
 
11107233531< 0.1%
 
11107233691< 0.1%
 
11107239731< 0.1%
 
11107245871< 0.1%
 
11107251541< 0.1%
 
11107253511< 0.1%
 
11107253711< 0.1%
 
11107264961< 0.1%
 
11107266811< 0.1%
 
ValueCountFrequency (%) 
11110841981< 0.1%
 
11110841681< 0.1%
 
11110841601< 0.1%
 
11110841301< 0.1%
 
11110840781< 0.1%
 
11110840381< 0.1%
 
11110839161< 0.1%
 
11110838941< 0.1%
 
11110838371< 0.1%
 
11110837521< 0.1%
 

county
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.8 KiB
Los Angeles
2772 
ValueCountFrequency (%) 
Los Angeles2772100.0%
 
2020-10-10T21:49:22.548749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-10T21:49:22.671623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-10T21:49:22.790246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length11
Mean length11
Min length11

Overview of Unicode Properties

Unique unicode characters9
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
s554418.2%
 
e554418.2%
 
L27729.1%
 
o27729.1%
 
27729.1%
 
A27729.1%
 
n27729.1%
 
g27729.1%
 
l27729.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter2217672.7%
 
Uppercase Letter554418.2%
 
Space Separator27729.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
L277250.0%
 
A277250.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
s554425.0%
 
e554425.0%
 
o277212.5%
 
n277212.5%
 
g277212.5%
 
l277212.5%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
2772100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin2772090.9%
 
Common27729.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
s554420.0%
 
e554420.0%
 
L277210.0%
 
o277210.0%
 
A277210.0%
 
n277210.0%
 
g277210.0%
 
l277210.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
2772100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII30492100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
s554418.2%
 
e554418.2%
 
L27729.1%
 
o27729.1%
 
27729.1%
 
A27729.1%
 
n27729.1%
 
g27729.1%
 
l27729.1%
 

subdivision
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct2235
Distinct (%)87.0%
Missing202
Missing (%)7.3%
Memory size21.8 KiB
1
 
12
61725
 
10
2
 
10
REDONDO VILLA TR
 
8
6170
 
7
Other values (2230)
2523 
ValueCountFrequency (%) 
1120.4%
 
61725100.4%
 
2100.4%
 
REDONDO VILLA TR80.3%
 
617070.3%
 
HERALD SECOND SUB60.2%
 
1110460.2%
 
100060.2%
 
4460060.2%
 
1350.2%
 
LONG BEACH50.2%
 
247450.2%
 
582240.1%
 
EL SEGUNDO40.1%
 
REDONDO BEACH40.1%
 
5402540.1%
 
726040.1%
 
599240.1%
 
2499730.1%
 
780330.1%
 
630.1%
 
3845430.1%
 
1621530.1%
 
849830.1%
 
SUNSHINE PLACE30.1%
 
Other values (2210)243988.0%
 
(Missing)2027.3%
 
2020-10-10T21:49:23.013731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2013 ?
Unique (%)78.3%
2020-10-10T21:49:23.263294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length30
Median length5
Mean length5.856060606
Min length1

Overview of Unicode Properties

Unique unicode characters43
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
113758.5%
 
212968.0%
 
311907.3%
 
411737.2%
 
510786.6%
 
09455.8%
 
69295.7%
 
98455.2%
 
78365.2%
 
88225.1%
 
5603.4%
 
A4863.0%
 
E4752.9%
 
n4042.5%
 
R3822.4%
 
O3262.0%
 
S3232.0%
 
N3041.9%
 
T2981.8%
 
L2931.8%
 
I2131.3%
 
a2021.2%
 
D1901.2%
 
C1711.1%
 
H1681.0%
 
Other values (18)9495.8%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1048964.6%
 
Uppercase Letter448827.6%
 
Lowercase Letter6063.7%
 
Space Separator5603.4%
 
Dash Punctuation690.4%
 
Other Punctuation210.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1137513.1%
 
2129612.4%
 
3119011.3%
 
4117311.2%
 
5107810.3%
 
09459.0%
 
69298.9%
 
98458.1%
 
78368.0%
 
88227.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n40466.7%
 
a20233.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A48610.8%
 
E47510.6%
 
R3828.5%
 
O3267.3%
 
S3237.2%
 
N3046.8%
 
T2986.6%
 
L2936.5%
 
I2134.7%
 
D1904.2%
 
C1713.8%
 
H1683.7%
 
B1232.7%
 
M1052.3%
 
U1022.3%
 
G972.2%
 
P942.1%
 
V861.9%
 
W771.7%
 
K661.5%
 
Y431.0%
 
F260.6%
 
J190.4%
 
X70.2%
 
Q70.2%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-69100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
560100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
&1781.0%
 
#29.5%
 
/29.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1113968.6%
 
Latin509431.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
1137512.3%
 
2129611.6%
 
3119010.7%
 
4117310.5%
 
510789.7%
 
09458.5%
 
69298.3%
 
98457.6%
 
78367.5%
 
88227.4%
 
5605.0%
 
-690.6%
 
&170.2%
 
#2< 0.1%
 
/2< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A4869.5%
 
E4759.3%
 
n4047.9%
 
R3827.5%
 
O3266.4%
 
S3236.3%
 
N3046.0%
 
T2985.9%
 
L2935.8%
 
I2134.2%
 
a2024.0%
 
D1903.7%
 
C1713.4%
 
H1683.3%
 
B1232.4%
 
M1052.1%
 
U1022.0%
 
G971.9%
 
P941.8%
 
V861.7%
 
W771.5%
 
K661.3%
 
Y430.8%
 
F260.5%
 
J190.4%
 
Other values (3)210.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII16233100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
113758.5%
 
212968.0%
 
311907.3%
 
411737.2%
 
510786.6%
 
09455.8%
 
69295.7%
 
98455.2%
 
78365.2%
 
88225.1%
 
5603.4%
 
A4863.0%
 
E4752.9%
 
n4042.5%
 
R3822.4%
 
O3262.0%
 
S3232.0%
 
N3041.9%
 
T2981.8%
 
L2931.8%
 
I2131.3%
 
a2021.2%
 
D1901.2%
 
C1711.1%
 
H1681.0%
 
Other values (18)9495.8%
 

census
Real number (ℝ≥0)

MISSING

Distinct8
Distinct (%)0.3%
Missing32
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean2.024452555
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size21.8 KiB
2020-10-10T21:49:23.446939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.117970583
Coefficient of variation (CV)0.5522335313
Kurtosis1.282579805
Mean2.024452555
Median Absolute Deviation (MAD)1
Skewness1.141042429
Sum5547
Variance1.249858225
MonotocityNot monotonic
2020-10-10T21:49:23.607041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
1112240.5%
 
283330.1%
 
349517.9%
 
42077.5%
 
5592.1%
 
6180.6%
 
750.2%
 
81< 0.1%
 
(Missing)321.2%
 
ValueCountFrequency (%) 
1112240.5%
 
283330.1%
 
349517.9%
 
42077.5%
 
5592.1%
 
6180.6%
 
750.2%
 
81< 0.1%
 
ValueCountFrequency (%) 
81< 0.1%
 
750.2%
 
6180.6%
 
5592.1%
 
42077.5%
 
349517.9%
 
283330.1%
 
1112240.5%
 

tract
Real number (ℝ≥0)

MISSING
ZEROS

Distinct1340
Distinct (%)48.9%
Missing32
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean439190.9485
Minimum0
Maximum980008
Zeros29
Zeros (%)1.0%
Memory size21.8 KiB
2020-10-10T21:49:23.807140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile111203.9
Q1203575
median408005
Q3602027.25
95-th percentile920020
Maximum980008
Range980008
Interquartile range (IQR)398452.25

Descriptive statistics

Standard deviation261219.6753
Coefficient of variation (CV)0.5947747242
Kurtosis-0.8826965353
Mean439190.9485
Median Absolute Deviation (MAD)195796.5
Skewness0.432389843
Sum1203383199
Variance6.823571877e+10
MonotocityNot monotonic
2020-10-10T21:49:24.038596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0291.0%
 
143500120.4%
 
577603100.4%
 
137103100.4%
 
92002890.3%
 
80010290.3%
 
90100490.3%
 
91100190.3%
 
80032980.3%
 
10810270.3%
 
90080570.3%
 
90080370.3%
 
10820270.3%
 
91021070.3%
 
14160070.3%
 
90120570.3%
 
92002070.3%
 
26120070.3%
 
92001570.3%
 
92010260.2%
 
70230060.2%
 
92004360.2%
 
92004260.2%
 
21270260.2%
 
92031360.2%
 
Other values (1315)252991.2%
 
(Missing)321.2%
 
ValueCountFrequency (%) 
0291.0%
 
10111020.1%
 
10112220.1%
 
10121020.1%
 
1012201< 0.1%
 
10130050.2%
 
10140030.1%
 
1021031< 0.1%
 
10210450.2%
 
1031011< 0.1%
 
ValueCountFrequency (%) 
9800081< 0.1%
 
9303011< 0.1%
 
9301011< 0.1%
 
92033930.1%
 
92033850.2%
 
92033430.1%
 
9203321< 0.1%
 
9203311< 0.1%
 
92033060.2%
 
92032920.1%
 

lot
Categorical

HIGH CARDINALITY
MISSING

Distinct346
Distinct (%)13.3%
Missing178
Missing (%)6.4%
Memory size21.8 KiB
1
382 
2
 
102
3
 
87
4
 
66
5
 
54
Other values (341)
1903 
ValueCountFrequency (%) 
138213.8%
 
21023.7%
 
3873.1%
 
4662.4%
 
5541.9%
 
11521.9%
 
6501.8%
 
7471.7%
 
9391.4%
 
14391.4%
 
16361.3%
 
15361.3%
 
18351.3%
 
10351.3%
 
12341.2%
 
8331.2%
 
17331.2%
 
13321.2%
 
19321.2%
 
24311.1%
 
21301.1%
 
23291.0%
 
20291.0%
 
35260.9%
 
25250.9%
 
Other values (321)120043.3%
 
(Missing)1786.4%
 
2020-10-10T21:49:24.300059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique156 ?
Unique (%)6.0%
2020-10-10T21:49:24.546920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length2
Mean length1.908369408
Min length1

Overview of Unicode Properties

Unique unicode characters18
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
1125923.8%
 
267212.7%
 
353010.0%
 
44318.1%
 
53737.1%
 
n3566.7%
 
63296.2%
 
73075.8%
 
83005.7%
 
02675.0%
 
92665.0%
 
a1783.4%
 
A80.2%
 
B60.1%
 
C40.1%
 
I2< 0.1%
 
Q1< 0.1%
 
E1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number473489.5%
 
Lowercase Letter53410.1%
 
Uppercase Letter220.4%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1125926.6%
 
267214.2%
 
353011.2%
 
44319.1%
 
53737.9%
 
63296.9%
 
73076.5%
 
83006.3%
 
02675.6%
 
92665.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n35666.7%
 
a17833.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A836.4%
 
B627.3%
 
C418.2%
 
I29.1%
 
Q14.5%
 
E14.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Common473489.5%
 
Latin55610.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
1125926.6%
 
267214.2%
 
353011.2%
 
44319.1%
 
53737.9%
 
63296.9%
 
73076.5%
 
83006.3%
 
02675.6%
 
92665.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n35664.0%
 
a17832.0%
 
A81.4%
 
B61.1%
 
C40.7%
 
I20.4%
 
Q10.2%
 
E10.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII5290100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
1125923.8%
 
267212.7%
 
353010.0%
 
44318.1%
 
53737.1%
 
n3566.7%
 
63296.2%
 
73075.8%
 
83005.7%
 
02675.0%
 
92665.0%
 
a1783.4%
 
A80.2%
 
B60.1%
 
C40.1%
 
I2< 0.1%
 
Q1< 0.1%
 
E1< 0.1%
 

zoning
Categorical

HIGH CARDINALITY

Distinct696
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Memory size21.8 KiB
LAR1
380 
LAR3
 
140
LARD1.5
 
79
LARS
 
72
SCUR2
 
65
Other values (691)
2036 
ValueCountFrequency (%) 
LAR138013.7%
 
LAR31405.1%
 
LARD1.5792.8%
 
LARS722.6%
 
SCUR2652.3%
 
LBR1N622.2%
 
LAR2471.7%
 
LARE11441.6%
 
LARD2441.6%
 
LARA341.2%
 
LARE15311.1%
 
LCR1YY260.9%
 
LAC2250.9%
 
LCA21*220.8%
 
TORR-LO220.8%
 
LCA25*220.8%
 
LCA11*210.8%
 
LCA22*200.7%
 
SCUR3200.7%
 
LKR1YY190.7%
 
LARD3190.7%
 
GLR1YY170.6%
 
LCR1*160.6%
 
LMR1*150.5%
 
MNRS150.5%
 
Other values (671)149553.9%
 
2020-10-10T21:49:24.774837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique399 ?
Unique (%)14.4%
2020-10-10T21:49:25.001618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length31
Median length5
Mean length5.612914863
Min length3

Overview of Unicode Properties

Unique unicode characters55
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
R252616.2%
 
L190112.2%
 
115169.7%
 
A14579.4%
 
09015.8%
 
*8815.7%
 
C8185.3%
 
25633.6%
 
S4833.1%
 
Y4432.8%
 
D4042.6%
 
P3782.4%
 
33572.3%
 
52891.9%
 
M2671.7%
 
B2481.6%
 
O2141.4%
 
E1891.2%
 
U1861.2%
 
41621.0%
 
N1450.9%
 
71410.9%
 
-1350.9%
 
G1150.7%
 
61010.6%
 
Other values (30)7394.7%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter1021965.7%
 
Decimal Number409126.3%
 
Other Punctuation10246.6%
 
Dash Punctuation1350.9%
 
Lowercase Letter650.4%
 
Open Punctuation100.1%
 
Close Punctuation100.1%
 
Connector Punctuation5< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
R252624.7%
 
L190118.6%
 
A145714.3%
 
C8188.0%
 
S4834.7%
 
Y4434.3%
 
D4044.0%
 
P3783.7%
 
M2672.6%
 
B2482.4%
 
O2142.1%
 
E1891.8%
 
U1861.8%
 
N1451.4%
 
G1151.1%
 
H1001.0%
 
W890.9%
 
T680.7%
 
F590.6%
 
V440.4%
 
I350.3%
 
K300.3%
 
Z190.2%
 
X1< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1151637.1%
 
090122.0%
 
256313.8%
 
33578.7%
 
52897.1%
 
41624.0%
 
71413.4%
 
61012.5%
 
9330.8%
 
8280.7%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
*88186.0%
 
.959.3%
 
/232.2%
 
:151.5%
 
&101.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-135100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(10100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)10100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
t1624.6%
 
o1116.9%
 
r913.8%
 
a710.8%
 
c57.7%
 
e57.7%
 
y46.2%
 
l23.1%
 
p23.1%
 
s23.1%
 
n11.5%
 
u11.5%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_5100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1028466.1%
 
Common527533.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
R252624.6%
 
L190118.5%
 
A145714.2%
 
C8188.0%
 
S4834.7%
 
Y4434.3%
 
D4043.9%
 
P3783.7%
 
M2672.6%
 
B2482.4%
 
O2142.1%
 
E1891.8%
 
U1861.8%
 
N1451.4%
 
G1151.1%
 
H1001.0%
 
W890.9%
 
T680.7%
 
F590.6%
 
V440.4%
 
I350.3%
 
K300.3%
 
Z190.2%
 
t160.2%
 
o110.1%
 
Other values (11)390.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
1151628.7%
 
090117.1%
 
*88116.7%
 
256310.7%
 
33576.8%
 
52895.5%
 
41623.1%
 
71412.7%
 
-1352.6%
 
61011.9%
 
.951.8%
 
9330.6%
 
8280.5%
 
/230.4%
 
:150.3%
 
(100.2%
 
)100.2%
 
&100.2%
 
_50.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII15559100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
R252616.2%
 
L190112.2%
 
115169.7%
 
A14579.4%
 
09015.8%
 
*8815.7%
 
C8185.3%
 
25633.6%
 
S4833.1%
 
Y4432.8%
 
D4042.6%
 
P3782.4%
 
33572.3%
 
52891.9%
 
M2671.7%
 
B2481.6%
 
O2141.4%
 
E1891.2%
 
U1861.2%
 
41621.0%
 
N1450.9%
 
71410.9%
 
-1350.9%
 
G1150.7%
 
61010.6%
 
Other values (30)7394.7%
 

date
Categorical

HIGH CARDINALITY

Distinct271
Distinct (%)9.8%
Missing12
Missing (%)0.4%
Memory size21.8 KiB
2020-09-25
420 
2020-09-17
396 
2020-09-18
389 
2020-09-24
369 
2020-09-21
256 
Other values (266)
930 
ValueCountFrequency (%) 
2020-09-2542015.2%
 
2020-09-1739614.3%
 
2020-09-1838914.0%
 
2020-09-2436913.3%
 
2020-09-212569.2%
 
2020-09-282147.7%
 
2020-09-231686.1%
 
2020-09-221505.4%
 
2020-09-161314.7%
 
1972-04-2020.1%
 
2000-04-2820.1%
 
2011-12-0620.1%
 
2020-09-1420.1%
 
1975-10-0920.1%
 
2000-03-291< 0.1%
 
2009-06-051< 0.1%
 
1973-05-231< 0.1%
 
1999-12-161< 0.1%
 
2019-11-141< 0.1%
 
2006-02-061< 0.1%
 
1980-06-251< 0.1%
 
1994-05-101< 0.1%
 
1999-07-131< 0.1%
 
2013-07-101< 0.1%
 
1985-08-261< 0.1%
 
Other values (246)2468.9%
 
(Missing)120.4%
 
2020-10-10T21:49:25.235457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique257 ?
Unique (%)9.3%
2020-10-10T21:49:25.435267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length9.96969697
Min length3

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0801229.0%
 
2704425.5%
 
-552020.0%
 
9275610.0%
 
116205.9%
 
87282.6%
 
74991.8%
 
54971.8%
 
44351.6%
 
32600.9%
 
62290.8%
 
n240.1%
 
a12< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number2208079.9%
 
Dash Punctuation552020.0%
 
Lowercase Letter360.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0801236.3%
 
2704431.9%
 
9275612.5%
 
116207.3%
 
87283.3%
 
74992.3%
 
54972.3%
 
44352.0%
 
32601.2%
 
62291.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-5520100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n2466.7%
 
a1233.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common2760099.9%
 
Latin360.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
0801229.0%
 
2704425.5%
 
-552020.0%
 
9275610.0%
 
116205.9%
 
87282.6%
 
74991.8%
 
54971.8%
 
44351.6%
 
32600.9%
 
62290.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n2466.7%
 
a1233.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII27636100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0801229.0%
 
2704425.5%
 
-552020.0%
 
9275610.0%
 
116205.9%
 
87282.6%
 
74991.8%
 
54971.8%
 
44351.6%
 
32600.9%
 
62290.8%
 
n240.1%
 
a12< 0.1%
 

sale_price
Real number (ℝ≥0)

MISSING

Distinct1014
Distinct (%)37.9%
Missing97
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean1097749.591
Minimum1000
Maximum75454545
Zeros0
Zeros (%)0.0%
Memory size21.8 KiB
2020-10-10T21:49:25.637985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile192800
Q1500000
median700000
Q31098500
95-th percentile2817050
Maximum75454545
Range75453545
Interquartile range (IQR)598500

Descriptive statistics

Standard deviation2231697.608
Coefficient of variation (CV)2.032975122
Kurtosis531.9059713
Mean1097749.591
Median Absolute Deviation (MAD)258000
Skewness19.03179199
Sum2936480155
Variance4.980474215e+12
MonotocityNot monotonic
2020-10-10T21:49:25.857149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
650000341.2%
 
600000271.0%
 
550000210.8%
 
700000200.7%
 
800000200.7%
 
500000190.7%
 
750000180.6%
 
450000180.6%
 
610000170.6%
 
350000170.6%
 
615000150.5%
 
400000150.5%
 
850000150.5%
 
680000150.5%
 
950000150.5%
 
410000150.5%
 
675000150.5%
 
510000140.5%
 
1150000140.5%
 
900000140.5%
 
575000140.5%
 
690000140.5%
 
1000000140.5%
 
1500000140.5%
 
630000130.5%
 
Other values (989)224881.1%
 
(Missing)973.5%
 
ValueCountFrequency (%) 
10001< 0.1%
 
15001< 0.1%
 
300020.1%
 
35001< 0.1%
 
40001< 0.1%
 
45001< 0.1%
 
500020.1%
 
55001< 0.1%
 
65001< 0.1%
 
75001< 0.1%
 
ValueCountFrequency (%) 
754545451< 0.1%
 
405000001< 0.1%
 
376250001< 0.1%
 
210630001< 0.1%
 
200000001< 0.1%
 
190000001< 0.1%
 
176940001< 0.1%
 
153750001< 0.1%
 
136300001< 0.1%
 
135000001< 0.1%
 

estimated_value
Real number (ℝ≥0)

MISSING

Distinct1194
Distinct (%)47.3%
Missing246
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean869786.2233
Minimum125000
Maximum2934000
Zeros0
Zeros (%)0.0%
Memory size21.8 KiB
2020-10-10T21:49:26.087689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum125000
5-th percentile347500
Q1539000
median702000
Q31027750
95-th percentile2082500
Maximum2934000
Range2809000
Interquartile range (IQR)488750

Descriptive statistics

Standard deviation515997.5933
Coefficient of variation (CV)0.5932464547
Kurtosis2.472468672
Mean869786.2233
Median Absolute Deviation (MAD)204000
Skewness1.644840452
Sum2197080000
Variance2.662535163e+11
MonotocityNot monotonic
2020-10-10T21:49:26.297828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
63200090.3%
 
68400080.3%
 
60600080.3%
 
60800080.3%
 
62500080.3%
 
58000080.3%
 
74800080.3%
 
81300080.3%
 
56800080.3%
 
87400070.3%
 
63100070.3%
 
61100070.3%
 
60000070.3%
 
56600070.3%
 
77900070.3%
 
69300070.3%
 
62700070.3%
 
59900070.3%
 
44700070.3%
 
55000070.3%
 
68700070.3%
 
62400070.3%
 
58200070.3%
 
47800070.3%
 
54700070.3%
 
Other values (1169)234184.5%
 
(Missing)2468.9%
 
ValueCountFrequency (%) 
1250001< 0.1%
 
1620001< 0.1%
 
1740001< 0.1%
 
1800001< 0.1%
 
1840001< 0.1%
 
1950001< 0.1%
 
1980001< 0.1%
 
1990001< 0.1%
 
2100001< 0.1%
 
2120001< 0.1%
 
ValueCountFrequency (%) 
29340001< 0.1%
 
28940001< 0.1%
 
28930001< 0.1%
 
287300020.1%
 
28630001< 0.1%
 
28410001< 0.1%
 
28330001< 0.1%
 
28060001< 0.1%
 
27990001< 0.1%
 
27870001< 0.1%
 

sex_offenders
Real number (ℝ≥0)

ZEROS

Distinct82
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.193362193
Minimum0
Maximum135
Zeros472
Zeros (%)17.0%
Memory size21.8 KiB
2020-10-10T21:49:26.517740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q39
95-th percentile23
Maximum135
Range135
Interquartile range (IQR)8

Descriptive statistics

Standard deviation12.3097629
Coefficient of variation (CV)1.711266939
Kurtosis31.04213757
Mean7.193362193
Median Absolute Deviation (MAD)3
Skewness4.853917691
Sum19940
Variance151.5302627
MonotocityNot monotonic
2020-10-10T21:49:26.745317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
047217.0%
 
136013.0%
 
329610.7%
 
22488.9%
 
51736.2%
 
41635.9%
 
61485.3%
 
71144.1%
 
91013.6%
 
8843.0%
 
10802.9%
 
11762.7%
 
12531.9%
 
13481.7%
 
15451.6%
 
14331.2%
 
18311.1%
 
16301.1%
 
17250.9%
 
19140.5%
 
21140.5%
 
22120.4%
 
23110.4%
 
2090.3%
 
2590.3%
 
Other values (57)1234.4%
 
ValueCountFrequency (%) 
047217.0%
 
136013.0%
 
22488.9%
 
329610.7%
 
41635.9%
 
51736.2%
 
61485.3%
 
71144.1%
 
8843.0%
 
91013.6%
 
ValueCountFrequency (%) 
1351< 0.1%
 
1321< 0.1%
 
1291< 0.1%
 
1151< 0.1%
 
1041< 0.1%
 
991< 0.1%
 
981< 0.1%
 
971< 0.1%
 
961< 0.1%
 
9420.1%
 

crime_index
Categorical

MISSING

Distinct7
Distinct (%)0.3%
Missing416
Missing (%)15.0%
Memory size21.8 KiB
Low
703 
Moderate
691 
Slightly High
689 
Very Low
191 
Moderately High
 
60
Other values (2)
 
22
ValueCountFrequency (%) 
Low70325.4%
 
Moderate69124.9%
 
Slightly High68924.9%
 
Very Low1916.9%
 
Moderately High602.2%
 
High190.7%
 
Very High30.1%
 
(Missing)41615.0%
 
2020-10-10T21:49:26.967631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-10T21:49:27.103644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-10T21:49:27.288810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length8
Mean length7.3495671
Min length3

Overview of Unicode Properties

Unique unicode characters19
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e16968.3%
 
o16458.1%
 
i14607.2%
 
g14607.2%
 
h14607.2%
 
t14407.1%
 
l14387.1%
 
a11675.7%
 
r9454.6%
 
y9434.6%
 
9434.6%
 
L8944.4%
 
w8944.4%
 
n8324.1%
 
H7713.8%
 
M7513.7%
 
d7513.7%
 
S6893.4%
 
V1941.0%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter1613179.2%
 
Uppercase Letter329916.2%
 
Space Separator9434.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
L89427.1%
 
H77123.4%
 
M75122.8%
 
S68920.9%
 
V1945.9%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e169610.5%
 
o164510.2%
 
i14609.1%
 
g14609.1%
 
h14609.1%
 
t14408.9%
 
l14388.9%
 
a11677.2%
 
r9455.9%
 
y9435.8%
 
w8945.5%
 
n8325.2%
 
d7514.7%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
943100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1943095.4%
 
Common9434.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e16968.7%
 
o16458.5%
 
i14607.5%
 
g14607.5%
 
h14607.5%
 
t14407.4%
 
l14387.4%
 
a11676.0%
 
r9454.9%
 
y9434.9%
 
L8944.6%
 
w8944.6%
 
n8324.3%
 
H7714.0%
 
M7513.9%
 
d7513.9%
 
S6893.5%
 
V1941.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
943100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII20373100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e16968.3%
 
o16458.1%
 
i14607.2%
 
g14607.2%
 
h14607.2%
 
t14407.1%
 
l14387.1%
 
a11675.7%
 
r9454.6%
 
y9434.6%
 
9434.6%
 
L8944.4%
 
w8944.4%
 
n8324.1%
 
H7713.8%
 
M7513.7%
 
d7513.7%
 
S6893.4%
 
V1941.0%
 

enviornmental_hazards
Real number (ℝ≥0)

Distinct52
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.039321789
Minimum1
Maximum84
Zeros0
Zeros (%)0.0%
Memory size21.8 KiB
2020-10-10T21:49:27.478514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median5
Q38
95-th percentile19
Maximum84
Range83
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.935562411
Coefficient of variation (CV)0.9852600319
Kurtosis18.51162824
Mean7.039321789
Median Absolute Deviation (MAD)2
Skewness3.438520338
Sum19513
Variance48.10202596
MonotocityNot monotonic
2020-10-10T21:49:27.691310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
345416.4%
 
244015.9%
 
438313.8%
 
62187.9%
 
52177.8%
 
71766.3%
 
81605.8%
 
91244.5%
 
10893.2%
 
11812.9%
 
12582.1%
 
13431.6%
 
14431.6%
 
1381.4%
 
16341.2%
 
19220.8%
 
15210.8%
 
18200.7%
 
21180.6%
 
22160.6%
 
17130.5%
 
23100.4%
 
3280.3%
 
3770.3%
 
2660.2%
 
Other values (27)732.6%
 
ValueCountFrequency (%) 
1381.4%
 
244015.9%
 
345416.4%
 
438313.8%
 
52177.8%
 
62187.9%
 
71766.3%
 
81605.8%
 
91244.5%
 
10893.2%
 
ValueCountFrequency (%) 
841< 0.1%
 
701< 0.1%
 
661< 0.1%
 
651< 0.1%
 
551< 0.1%
 
521< 0.1%
 
481< 0.1%
 
471< 0.1%
 
4520.1%
 
431< 0.1%
 
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.8 KiB
1
2163 
2
567 
0
 
36
3
 
6
ValueCountFrequency (%) 
1216378.0%
 
256720.5%
 
0361.3%
 
360.2%
 
2020-10-10T21:49:27.907598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-10T21:49:28.023712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-10T21:49:28.162693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
1216378.0%
 
256720.5%
 
0361.3%
 
360.2%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number2772100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1216378.0%
 
256720.5%
 
0361.3%
 
360.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Common2772100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
1216378.0%
 
256720.5%
 
0361.3%
 
360.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2772100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
1216378.0%
 
256720.5%
 
0361.3%
 
360.2%
 

school quality
Categorical

MISSING

Distinct4
Distinct (%)0.1%
Missing41
Missing (%)1.5%
Memory size21.8 KiB
Average
967 
Excellent
748 
Above Average
715 
Poor
301 
ValueCountFrequency (%) 
Average96734.9%
 
Excellent74827.0%
 
Above Average71525.8%
 
Poor30110.9%
 
(Missing)411.5%
 
2020-10-10T21:49:28.341334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-10T21:49:28.475732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-10T21:49:28.631407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length9
Mean length8.702380952
Min length3

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e557523.1%
 
A23979.9%
 
v23979.9%
 
r19838.2%
 
a17237.1%
 
g16827.0%
 
l14966.2%
 
o13175.5%
 
n8303.4%
 
E7483.1%
 
x7483.1%
 
c7483.1%
 
t7483.1%
 
b7153.0%
 
7153.0%
 
P3011.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter1996282.8%
 
Uppercase Letter344614.3%
 
Space Separator7153.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A239769.6%
 
E74821.7%
 
P3018.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e557527.9%
 
v239712.0%
 
r19839.9%
 
a17238.6%
 
g16828.4%
 
l14967.5%
 
o13176.6%
 
n8304.2%
 
x7483.7%
 
c7483.7%
 
t7483.7%
 
b7153.6%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
715100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin2340897.0%
 
Common7153.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e557523.8%
 
A239710.2%
 
v239710.2%
 
r19838.5%
 
a17237.4%
 
g16827.2%
 
l14966.4%
 
o13175.6%
 
n8303.5%
 
E7483.2%
 
x7483.2%
 
c7483.2%
 
t7483.2%
 
b7153.1%
 
P3011.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
715100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII24123100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e557523.1%
 
A23979.9%
 
v23979.9%
 
r19838.2%
 
a17237.1%
 
g16827.0%
 
l14966.2%
 
o13175.5%
 
n8303.4%
 
E7483.1%
 
x7483.1%
 
c7483.1%
 
t7483.1%
 
b7153.0%
 
7153.0%
 
P3011.2%
 

url
URL

Distinct2770
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size21.8 KiB
https://www.realtytrac.com/property/ca/west-covina/91791/2107-abrazo-dr/34889447/
 
2
https://www.realtytrac.com/property/ca/newhall/91321/24421-shadeland-dr/154460872/
 
2
https://www.realtytrac.com/property/ca/black-butte/93591/vacvic-avenue-r4225-ste/241237366/
 
1
https://www.realtytrac.com/property/ca/los-angeles/90034/2851-s-corning-st/141365983/
 
1
https://www.realtytrac.com/property/ca/hacienda-heights/91745/3121-rio-lempa-dr/141269368/
 
1
Other values (2765)
2765 
ValueCountFrequency (%) 
https://www.realtytrac.com/property/ca/west-covina/91791/2107-abrazo-dr/34889447/20.1%
 
https://www.realtytrac.com/property/ca/newhall/91321/24421-shadeland-dr/154460872/20.1%
 
https://www.realtytrac.com/property/ca/black-butte/93591/vacvic-avenue-r4225-ste/241237366/1< 0.1%
 
https://www.realtytrac.com/property/ca/los-angeles/90034/2851-s-corning-st/141365983/1< 0.1%
 
https://www.realtytrac.com/property/ca/hacienda-heights/91745/3121-rio-lempa-dr/141269368/1< 0.1%
 
https://www.realtytrac.com/property/ca/monrovia/91016/2515-rochelle-ave/140751136/1< 0.1%
 
https://www.realtytrac.com/property/ca/w-hollywood/90069/8401-fountain-ave-5/24351246/1< 0.1%
 
https://www.realtytrac.com/property/ca/whittier/90605/13902-carnell-st/154943936/1< 0.1%
 
https://www.realtytrac.com/property/ca/valencia/91354/22705-rio-reyes-ct/18581534/1< 0.1%
 
https://www.realtytrac.com/property/ca/arcadia/91007/618-fairview-ave-107/154804369/1< 0.1%
 
https://www.realtytrac.com/property/ca/long-beach/90805/3051-e-69th-st/53149704/1< 0.1%
 
https://www.realtytrac.com/property/ca/santa-monica/90403/1122-26th-st-104/206742774/1< 0.1%
 
https://www.realtytrac.com/property/ca/los-angeles/90066/11417-culver-blvd-2/155006780/1< 0.1%
 
https://www.realtytrac.com/property/ca/torrance/90501/1762-w-244th-st/34816336/1< 0.1%
 
https://www.realtytrac.com/property/ca/van-nuys/91405/7449-murietta-ave/19271494/1< 0.1%
 
https://www.realtytrac.com/property/ca/avalon/90704/59-avalon-terrace-rd/154907394/1< 0.1%
 
https://www.realtytrac.com/property/ca/newhall/91321/19319-oak-plaza-ct/43865671/1< 0.1%
 
https://www.realtytrac.com/property/ca/los-angeles/90043/6119-eileen-ave/45952689/1< 0.1%
 
https://www.realtytrac.com/property/ca/chatsworth/91311/9134-foster-ln/325251183/1< 0.1%
 
https://www.realtytrac.com/property/ca/lakewood/90713/4552-ocana-ave/51904419/1< 0.1%
 
https://www.realtytrac.com/property/ca/sylmar/91342/16059-circle-diamond-rd/2988316/1< 0.1%
 
https://www.realtytrac.com/property/ca/santa-clarita/91387/15036-live-oak-springs-canyon-rd/23988854/1< 0.1%
 
https://www.realtytrac.com/property/ca/hermosa-beach/90254/1830-rhodes-st/154520683/1< 0.1%
 
https://www.realtytrac.com/property/ca/west-covina/91790/1530-s-siesta-ave/33969704/1< 0.1%
 
https://www.realtytrac.com/property/ca/los-angeles/90059/1258-e-108th-st/21850797/1< 0.1%
 
Other values (2745)274599.0%
 
ValueCountFrequency (%) 
https2772100.0%
 
ValueCountFrequency (%) 
www.realtytrac.com2772100.0%
 
ValueCountFrequency (%) 
/property/ca/newhall/91321/24421-shadeland-dr/154460872/20.1%
 
/property/ca/west-covina/91791/2107-abrazo-dr/34889447/20.1%
 
/property/ca/encino/91316/4060-falling-leaf-dr/19027234/1< 0.1%
 
/property/ca/sun-valley/91352/7215-clybourn-ave/43702269/1< 0.1%
 
/property/ca/lancaster/93536/4517-w-avenue-j12/154757580/1< 0.1%
 
/property/ca/pasadena/91105/442-s-raymond-ave/154793680/1< 0.1%
 
/property/ca/los-angeles/90025/10348-calvin-ave/40209949/1< 0.1%
 
/property/ca/lancaster/93534/330-w-pillsbury-st/154474826/1< 0.1%
 
/property/ca/burbank/91505/841-n-california-st/150417159/1< 0.1%
 
/property/ca/glendale/91206/2808-e-chevy-chase-dr/53571589/1< 0.1%
 
/property/ca/reseda/91335/19622-haynes-st/24499457/1< 0.1%
 
/property/ca/altadena/91001/1260-e-altadena-dr/151110089/1< 0.1%
 
/property/ca/sylmar/91342/14647-nurmi-st/54753916/1< 0.1%
 
/property/ca/whittier/90605/13413-lakeland-rd/16534776/1< 0.1%
 
/property/ca/studio-city/91604/11438-sunshine-ter/148504240/1< 0.1%
 
/property/ca/whittier/90601/11013-monte-vista-dr/53480152/1< 0.1%
 
/property/ca/agoura-hills/91301/4240-lost-hills-rd-2105/45924067/1< 0.1%
 
/property/ca/pico-rivera/90660/9635-marjorie-st/24738132/1< 0.1%
 
/property/ca/claremont/91711/777-n-indian-hill-blvd/154727002/1< 0.1%
 
/property/ca/torrance/90505/2722-sepulveda-blvd/223602954/1< 0.1%
 
/property/ca/los-angeles/90042/6453-n-figueroa-st/4552875/1< 0.1%
 
/property/ca/quartz-hill/93536/vacvic-70th-stwave-l4/241251625/1< 0.1%
 
/property/ca/montebello/90640/906-michelle-ct-4/34813698/1< 0.1%
 
/property/ca/montebello/90640/401-n-montebello-blvd/142716230/1< 0.1%
 
/property/ca/north-hollywood/91602/4525-lemp-ave/43872457/1< 0.1%
 
Other values (2745)274599.0%
 
ValueCountFrequency (%) 
2772100.0%
 
ValueCountFrequency (%) 
2772100.0%
 

bedrooms
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct24
Distinct (%)0.9%
Missing218
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean3.361002349
Minimum1
Maximum96
Zeros0
Zeros (%)0.0%
Memory size21.8 KiB
2020-10-10T21:49:28.820097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile5
Maximum96
Range95
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.989808766
Coefficient of variation (CV)0.8895586659
Kurtosis485.2100441
Mean3.361002349
Median Absolute Deviation (MAD)1
Skewness18.17592426
Sum8584
Variance8.938956457
MonotocityNot monotonic
2020-10-10T21:49:28.993641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%) 
3101636.7%
 
258621.1%
 
455620.1%
 
51605.8%
 
11214.4%
 
6421.5%
 
8180.6%
 
7140.5%
 
1080.3%
 
1270.3%
 
960.2%
 
1630.1%
 
1430.1%
 
1720.1%
 
1120.1%
 
2020.1%
 
301< 0.1%
 
281< 0.1%
 
151< 0.1%
 
961< 0.1%
 
241< 0.1%
 
261< 0.1%
 
291< 0.1%
 
731< 0.1%
 
(Missing)2187.9%
 
ValueCountFrequency (%) 
11214.4%
 
258621.1%
 
3101636.7%
 
455620.1%
 
51605.8%
 
6421.5%
 
7140.5%
 
8180.6%
 
960.2%
 
1080.3%
 
ValueCountFrequency (%) 
961< 0.1%
 
731< 0.1%
 
301< 0.1%
 
291< 0.1%
 
281< 0.1%
 
261< 0.1%
 
241< 0.1%
 
2020.1%
 
1720.1%
 
1630.1%
 

bathrooms
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct22
Distinct (%)0.9%
Missing218
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean2.613938919
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Memory size21.8 KiB
2020-10-10T21:49:29.172359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile5
Maximum99
Range98
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.98037344
Coefficient of variation (CV)1.140184806
Kurtosis529.4523464
Mean2.613938919
Median Absolute Deviation (MAD)1
Skewness19.33643734
Sum6676
Variance8.882625842
MonotocityNot monotonic
2020-10-10T21:49:29.345726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%) 
2104837.8%
 
373626.6%
 
145616.5%
 
41696.1%
 
5592.1%
 
6381.4%
 
7100.4%
 
890.3%
 
970.3%
 
1050.2%
 
1630.1%
 
1520.1%
 
1720.1%
 
3020.1%
 
111< 0.1%
 
121< 0.1%
 
471< 0.1%
 
201< 0.1%
 
141< 0.1%
 
221< 0.1%
 
651< 0.1%
 
991< 0.1%
 
(Missing)2187.9%
 
ValueCountFrequency (%) 
145616.5%
 
2104837.8%
 
373626.6%
 
41696.1%
 
5592.1%
 
6381.4%
 
7100.4%
 
890.3%
 
970.3%
 
1050.2%
 
ValueCountFrequency (%) 
991< 0.1%
 
651< 0.1%
 
471< 0.1%
 
3020.1%
 
221< 0.1%
 
201< 0.1%
 
1720.1%
 
1630.1%
 
1520.1%
 
141< 0.1%
 

Interactions

2020-10-10T21:48:21.795630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-10T21:48:29.712995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-10T21:48:29.925573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-10T21:49:00.921319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-10T21:49:04.520436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-10T21:49:04.885797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-10T21:49:05.054835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-10T21:49:13.645554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
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Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
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Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-10T21:49:31.076499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-10T21:49:14.208828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-10T21:49:15.851447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-10T21:49:16.610737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-10T21:49:17.293449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

latitudelongitudeaddressproperty_typehome_sizelot_sizeyear_builtparcel_numberrealtyIDcountysubdivisioncensustractlotzoningdatesale_priceestimated_valuesex_offenderscrime_indexenviornmental_hazardsnatural_disastersschool qualityurlbedroomsbathrooms
033.974622-118.1336796224 Nye StCondominium1357.022215.61988.063570120431.111054e+09Los Angeles453511.0532304.01CMR32020-09-28469000.0439000.07High151Averagehttps://www.realtytrac.com/property/ca/commerce/90040/6224-nye-st/54129244/3.03.0
134.156562-118.3968914723 Laurel Canyon BlvdRestaurant2250.09799.01959.023560370341.111055e+09Los Angeles73601.0143400.010LAC22020-09-281910000.0NaN5NaN61Excellenthttps://www.realtytrac.com/property/ca/valley-village/91607/4723-laurel-canyon-blvd/154414596/NaNNaN
234.677777-118.45116518118 Elizabeth Lake RdApartment house (5+ units)1454.013635.01948.032420150231.111056e+09Los AngelesNaN2.0920102.04LCC4*2020-09-28325000.0NaN0NaN21NaNhttps://www.realtytrac.com/property/ca/lake-hughes/93532/18118-elizabeth-lake-rd/251911403/7.06.0
334.072960-118.0668989259 Ramona BlvdSingle Family Residence1682.07000.01978.085940270161.111056e+09Los AngelesROSEMEAD2.0432901.06RMPOD*2020-09-28738000.0752000.03Moderate111Excellenthttps://www.realtytrac.com/property/ca/rosemead/91770/9259-ramona-blvd/154986110/3.02.0
433.777716-118.154913825 Obispo AveTriplex (3 units, any combination)1958.06754.01938.072580130161.111056e+09Los Angeles12.0576904.018LBR2N2020-09-281185000.0989000.011Slightly High81Averagehttps://www.realtytrac.com/property/ca/long-beach/90804/825-obispo-ave/44027788/4.03.0
533.886322-118.2513172201 W Reeve StSingle Family Residence1180.06216.01950.061400310211.111057e+09Los Angeles159817.0543100.0311CORL*2020-09-28560000.0537000.020High141Poorhttps://www.realtytrac.com/property/ca/compton/90220/2201-w-reeve-st/140044396/3.01.0
634.191476-118.3715136642 Ensign AveSingle Family Residence1296.05843.01944.023190190181.111057e+09Los Angeles131681.0123206.05LAR12020-09-28715000.0699000.015Low141Averagehttps://www.realtytrac.com/property/ca/north-hollywood/91606/6642-ensign-ave/13931881/3.02.0
734.155054-118.239413622 Naranja DrQuadplex (4 Units, Any Combination)4764.06395.01930.056460160071.111057e+09Los Angeles92582.0301900.03GLR4YY2020-09-281475000.01474000.01Low41Above Averagehttps://www.realtytrac.com/property/ca/glendale/91206/622-naranja-dr/143877681/8.08.0
834.229076-118.6012888726 Owensmouth AveApartment house (5+ units)5568.09571.01962.027790410051.111058e+09Los Angeles253163.0113233.05LAR32020-09-281480000.0779000.02NaN61Averagehttps://www.realtytrac.com/property/ca/canoga-park/91304/8726-owensmouth-ave/154455515/8.08.0
934.686700-118.237500Vac/oldfield St/vic 63rd StwResidential - Vacant LandNaN6648.0NaN32030640321.111059e+09Los AngelesNaN4.0920328.078LRR12020-09-28442000.0NaN2Moderate22Averagehttps://www.realtytrac.com/property/ca/lancaster/93536/vacoldfield-stvic-63rd-stw/251911299/NaNNaN

Last rows

latitudelongitudeaddressproperty_typehome_sizelot_sizeyear_builtparcel_numberrealtyIDcountysubdivisioncensustractlotzoningdatesale_priceestimated_valuesex_offenderscrime_indexenviornmental_hazardsnatural_disastersschool qualityurlbedroomsbathrooms
276233.772038-118.192774115 W 4th St # 212Condominium1043.023086.81929.072800090981.110890e+09Los Angeles531711.0575902.01Lot:12020-09-18490000.0584000.019Slightly High131Averagehttps://www.realtytrac.com/property/ca/long-beach/90802/115-w-4th-st-212/2868592/NaNNaN
276334.612271-118.1637971808 W Avenue O4Single Family Residence3568.0108464.41982.030050070031.110890e+09Los AngelesNaN2.0910202.018LCA22*2020-09-18750000.0759000.01Moderate31Averagehttps://www.realtytrac.com/property/ca/palmdale/93551/1808-w-avenue-o4/40215785/4.03.0
276434.677778-118.1089481156 E Avenue J12Planned Unit Development (PUD)1310.02555.01981.031480210651.110891e+09Los Angeles394222.0900505.065LRRPD75007U*2020-09-18235000.0212000.013Moderate21Averagehttps://www.realtytrac.com/property/ca/lancaster/93535/1156-e-avenue-j12/30988391/3.02.0
276534.091631-118.121981411 S Monterey StTriplex (3 units, any combination)2840.07752.01985.053450100341.110891e+09Los AngelesHALL TR2.0481002.014ALRPD*2020-09-181300000.01088000.05Low131Excellenthttps://www.realtytrac.com/property/ca/alhambra/91801/411-s-monterey-st/154617236/5.05.0
276634.010260-118.44986412901 Warren AveSingle Family Residence3715.06125.01953.042470070191.110891e+09Los Angeles181402.0271400.0118LAR12020-09-183100000.02391000.04Slightly High71Averagehttps://www.realtytrac.com/property/ca/los-angeles/90066/12901-warren-ave/1263949/4.04.0
276734.135447-117.828819123 S Lone Hill AveSingle Family Residence1456.09545.01961.086600260341.110891e+09Los Angeles225791.0400402.03GDE52020-09-18745000.0735000.01Low31Excellenthttps://www.realtytrac.com/property/ca/glendora/91741/123-s-lone-hill-ave/52732653/3.02.0
276833.769697-118.122772348 Flint AveSingle Family Residence1778.03294.01977.072460250521.110892e+09Los AngelesMANILA AVE TR2.0577603.02LBPD12020-09-181020000.01045000.01Slightly High52Excellenthttps://www.realtytrac.com/property/ca/long-beach/90814/348-flint-ave/146881332/3.03.0
276934.012481-118.108985836 W Cleveland AveSingle Family Residence1704.05902.01939.063450190131.110894e+09Los Angeles56621.0530102.038MNR2YY2020-09-18690000.0567000.015Moderate61Averagehttps://www.realtytrac.com/property/ca/montebello/90640/836-w-cleveland-ave/141401861/4.02.0
2770NaNNaN25114 Orange LnCondominiumNaNNaNNaN28410770861.110894e+09Los AngelesNaNNaNNaN1SCUR22020-09-18679000.0NaN0Low21NaNhttps://www.realtytrac.com/property/ca/santa-clarita/91387/25114-orange-ln/328287129/NaNNaN
277133.904157-118.1532758334 Wilbarn StSingle Family Residence1175.04058.01961.062650280431.110894e+09Los AngelesCALIFORNIA CO OP COLONY TR1.0553504.09PARM*2020-09-18417000.0478000.07Slightly High132Averagehttps://www.realtytrac.com/property/ca/paramount/90723/8334-wilbarn-st/10888302/2.01.0

Duplicate rows

Most frequent

latitudelongitudeaddressproperty_typehome_sizelot_sizeyear_builtparcel_numberrealtyIDcountysubdivisioncensustractlotzoningdatesale_priceestimated_valuesex_offenderscrime_indexenviornmental_hazardsnatural_disastersschool qualityurlbedroomsbathroomscount
034.37167-118.53315424421 Shadeland DrSingle Family Residence1920.010014.01961.028270210121.111005e+09Los Angeles259923.0920312.012SCUR22020-09-24685000.0744000.01Low42Excellenthttps://www.realtytrac.com/property/ca/newhall/91321/24421-shadeland-dr/154460872/4.03.02